Abstract
Multivariate visualization techniques are often used as assistant tools for classification tasks up to now. However, few classification systems fully utilize the capability of multivariate visualization and integrate them with multivariate analysis algorithms into a compact system. We propose an interactive visual classification model based on some multivariate graphical presentation in this paper. As an example of it, a visual classifier based on parallel coordinates plot is developed. The multivariate data is first mapped to the parallel coordinates plot, and then an optimizer based on linear discriminant analysis optimizes it into the visualization more fit for classification tasks. This optimized visualization then can be processed by decision tree algorithm and attain classification rules. It has the merit of making the invisible visible and users can steer the classification process, consequently favor the understanding and knowledge discovery of original data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ankerst, M., Elsen, C., Ester, M., Kriegel, H.P.: Visual Classification: an Interactive Aapproach to Decision Tree Construction. In: Proc.5th Intl. Conf. On knowledge Discovery and Data Mining (KDD’99), pp. 392–396 (1999)
Ankerst, M., Ester, M., Kriegel, H. -P.: Towards an Effective Cooperation of the User and the Computer for Classification. In: Proc. Int’l Conf. Knowledge Discovery and Data Mining (ACM SIGKDD’00), pp. 179–188 (2000)
Teoh, S.T., Ma, K.-L.: StarClass: Interactive Visual Classification Using Star Coordinates. In: Proceedings of the 3rd SIAM International Conference on Data Mining (2003)
Teoh, S.T., Ma, K.-L.: PaintingClass: Interactive Construction, Visualization and Exploration of Decision Trees. In: Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, ACM Press, New York (2003)
Liu, D., Sprague, A., Gray, J.: PolyCluster: an Interactive Visualization Approach to Construct Classification Rules. In: International Conference on Machine Learning and Applications (ICMLA), Louisville, KY, pp. 280–287 (December 2004)
Inselberg, A.: The Plane with Parallel Coordinates. The Visual Computer 1, 69–91 (1985)
Wegman, E.: Hyper-dimensional Data Analysis Using Parallel Coordinates. Journal of the American Statistical Association, 664–675 (1985)
Darinka, B.-V., Zdenka, C.K., Marjana, N.: Multivariate Data Analysis in Classification of Vegetable Oils Characterized by the Content of Fatty Acids. Chemometrics and Intelligent Laboratory Systems 75, 31–43 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Y., Hong, W., Chen, N., Li, X., Liu, W., Zhang, T. (2007). Parallel Filter: A Visual Classifier Based on Parallel Coordinates and Multivariate Data Analysis. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_121
Download citation
DOI: https://doi.org/10.1007/978-3-540-74205-0_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
eBook Packages: Computer ScienceComputer Science (R0)